R Notebook

library(shiny)
library(tidyverse)
library(shinythemes)

Warning: package ‘shinythemes’ was built under R version 4.1.2

library(stringi)
library(RColorBrewer)

seabirds_cleaned_data <- read_csv("clean_data/seabirds_cleaned_data.csv")

Rows: 49020 Columns: 52
-- Column specification ---------------------------------------------------
Delimiter: ","
chr  (19): common_name, scientific_name, species_abbreviation, age, plp...
dbl  (28): record_x, record_id, wanplum, total_sighting, num_feeding, n...
lgl   (3): sex, air_temp, salinity
dttm  (2): date, time

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

birds_21 <- seabirds_cleaned_data %>%
  mutate(bird_type = case_when(
  str_detect(common_name, 
             regex("shearwater", 
                   ignore_case = TRUE)) ~ "Shearwater",
  str_detect(common_name, 
             regex("albatross", 
                   ignore_case = TRUE)) ~ "Albatross",
  str_detect(common_name, 
             regex("mollymawk", 
                   ignore_case = TRUE)) ~ "Mollymawk",
  str_detect(common_name, 
             regex("petrel", 
                   ignore_case = TRUE)) ~ "Petrel",
  str_detect(common_name, 
             regex("prion", 
                   ignore_case = TRUE)) ~ "Prion",
  str_detect(common_name, 
             regex("skua", 
                   ignore_case = TRUE)) ~ "Skua",
  str_detect(common_name, 
             regex("penguin", 
                   ignore_case = TRUE)) ~ "Penguin",
  str_detect(common_name, 
             regex("tropicbird", 
                   ignore_case = TRUE)) ~ "Tropicbird",
  str_detect(common_name, 
             regex("noddy", 
                   ignore_case = TRUE)) ~ "Noddy",
  str_detect(common_name, 
             regex("tern", 
                   ignore_case = TRUE)) ~ "Tern",
  str_detect(common_name, 
             regex("gull", 
                   ignore_case = TRUE)) ~ "Gull",
  str_detect(common_name, 
             regex("booby", 
                   ignore_case = TRUE)) ~ "Booby",
  str_detect(common_name, 
             regex("frigatebird", 
                   ignore_case = TRUE)) ~ "Frigatebird",
  str_detect(common_name, 
             regex("shag", 
                   ignore_case = TRUE)) ~ "Shag",
  str_detect(common_name, 
             regex("sheathbill", 
                   ignore_case = TRUE)) ~ "Sheathbill",
  str_detect(common_name, 
             regex("fulmar", 
                   ignore_case = TRUE)) ~ "Fulmar",
  str_detect(common_name, 
             regex("gannet", 
                   ignore_case = TRUE)) ~ "Gannet",
  str_detect(common_name, 
             regex("cormorant", 
                   ignore_case = TRUE)) ~ "Cormorant",
  str_detect(common_name, 
             regex("procellaria", 
                   ignore_case = TRUE)) ~ "Procellaria",
    TRUE ~ common_name))

birds_21

birds_21 %>% 
  arrange(date)

# https://r-charts.com/color-palette-generator/

# https://www.statology.org/color-by-factor-ggplot2/

birds_pal <- c("#50e2ea", "#4edae5", "#4bd2df", "#49cada", "#47c2d4", 
               "#45bbcf", "#42b3c9", "#40abc4", "#3ea3be", "#3b9bb9",
               "#3993b3", "#378bae", "#3483a8", "#327ba3", "#30739d", 
               "#2e6c98", "#2b6492", "#295c8d", "#275487", "#244c82", "#22447c")

names(birds_pal) <- levels(birds_21$bird_type)

custom_colors <- scale_colour_manual(values = birds_pal)
  

birds <- c("Tropicbird" = "#50e2ea", "Tern" = "#4edae5", "Skua" = "#4bd2df", 
           "Sheathbill" = "#49cada", "Shearwater" = "#47c2d4", 
           "Shag" = "#45bbcf", "Seabird" = "#42b3c9", "Procellaria" = "#40abc4",
           "Prion" = "#3ea3be", "Petrel" = "#3b9bb9", "Penguin" = "#3993b3", 
           "Noddy" = "#378bae", "Mollymawk" = "#3483a8", "Jaeger" = "#327ba3", 
           "Gull" = "#30739d", "Gannet" = "#2e6c98", "Fulmar" = "#2b6492", 
           "Frigatebird" = "#295c8d", "Cormorant" = "#275487", 
           "Booby" = "#244c82", "Albatross" = "#22447c")

Jaeger Seabird

birds_21 %>% 
  filter(!is.na(bird_type)) %>% 
  count(bird_type)  

NA

Bird Seen tab

not updated birds so have 3 extra with no colour asigned, shiny up to date

sighting <-  birds_21 %>% 
            filter(!is.na(bird_type)) %>% 
            group_by(bird_type) %>% 
            summarise(count = sum(total_sighting, na.rm = TRUE)) %>%
            mutate(sighting_id = row_number())

sighting %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 5, 10, 1000, 6000, 1400000),
                       limits = c(1,1400000), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen \n Log10 scale") +
    scale_fill_manual(values = birds)

# log10() as 1 or more birds are less than 10 and don't show on normal graph

1,394,468

feeding <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(feeding, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(feeding_id = row_number())

feeding %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 5, 10, 100, 300, 800),
                       limits = c(1,800), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Feeding \n Log10 scale") +
    scale_fill_manual(values = birds)

# log10() as 1 or more birds are less than 10 and don't show on normal graph

on_ship <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(on_ship, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(on_ship_id = row_number())

on_ship %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 2, 3, 5, 7, 10, 60),
                       limits = c(1,60), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen On Ship") +
    scale_fill_manual(values = birds)

in_hand <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(in_hand, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(in_hand_id = row_number()) 

in_hand %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen In Hand") +
    scale_fill_manual(values = birds)

# https://stackoverflow.com/questions/14255533/pretty-ticks-for-log-normal-scale-using-ggplot2-dynamic-not-manual
# https://stackoverflow.com/questions/43974892/dynamic-limits-and-breaks-in-scale-y-continuous


fly_by <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(fly_by, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(fly_by_id = row_number())


# base_breaks <- function(n = 10){
#     function(x) {
#         axisTicks(log10(range(fly_by$count, na.rm = TRUE)), 
#                   log = if_else(max(fly_by$count) > 1000, TRUE, FALSE), n = n)
#     }
# }

# asd <- if_else(max(fly_by$count) < 1000, c(limits=c(0,max(fly_by$count)), 
#                                            breaks  = seq(0,max(fly_by$count),
#                                             by = round(max(fly_by$count)/5))), 
#                c(limits=c(0,max(fly_by$count)), 
#                  breaks  = seq(0,max(fly_by$count), 
#                   by = round(max(fly_by$count)/5)),
#                  trans = "log10")
#                )

fly_by %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(limits=c(0,max(fly_by$count)), 
                       breaks  = c(seq(0,max(fly_by$count),
                        by = (max(fly_by$count)/5))), trans = "log10"
                       #validate(max(fly_by$count) < 1000, trans = "log10")
                       ) +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds)

Error in seq.default(a, b, length.out = n + 1) : 
  'from' must be a finite number

breaks = c(1, 5, 10, 1000, 6000), limits = c(1,6000), trans = “log10”

Tab 2 Variants tab

variants <- birds_21 %>% 
              filter(bird_type == "Albatross") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal)            

variants <- birds_21 %>% 
              filter(bird_type == "Booby") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous( 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal)  

variants <- birds_21 %>% 
              filter(bird_type == "Cormorant") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous( 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal) 

variants <- birds_21 %>% 
              filter(bird_type == "Tropicbird") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous() +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal) 

Tab 3 Sightings tab tab

library(shiny)
library(tidyverse)
library(shinythemes)


#seabirds_cleaned_data <- read_csv("data/seabirds_cleaned_data.csv")

# birds_9 <- seabirds_cleaned_data %>% 
#   group_by(common_name) %>% 
#   mutate(common_name = if_else(str_detect(common_name, 
#                                           "(?i)shearwater"),"Shearwater", 
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)albatross"), "Albatross",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)mollymawk"), "Mollymawk",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)petrel"), "Petrel",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)prion"), "Prion",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)skua"), "Skua",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)penguin"), "Penguin",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)Red-tailed tropicbird"), 
#                                "Red-tailed tropicbird",
#                                common_name),
#          common_name = if_else(str_detect(common_name, 
#                                           "(?i)Brown noddy"), "Brown noddy",
#                                common_name)
#   ) %>% 
#   filter(common_name %in% c("Shearwater", "Albatross", 
#                             "Mollymawk", "Petrel", 
#                             "Prion", "Skua", 
#                             "Penguin", "Brown noddy", 
#                             "Red-tailed tropicbird"))

# pal <- c("Shearwater" = "grey", "Albatross" = "blue", 
#          "Mollymawk" = "yellow", "Petrel" = "green", 
#          "Prion" = "pink", "Skua" = "purple", 
#          "Penguin" = "orange", "Brown noddy" = "brown", 
#          "Red-tailed tropicbird" = "red")

# names(birds_9)
# head(birds_9)

# birds_9 %>% 
#   group_by(common_name) %>% 
#   mutate(feeding = if_else(feeding %in% "YES", 1, 0),
#          on_ship = if_else(on_ship %in% "YES", 1, 0),
#          in_hand = if_else(in_hand %in% "YES", 1, 0),
#          fly_by = if_else(fly_by %in% "YES", 1, 0)) %>% 
#   summarise(sighting_count = sum(total_sighting, na.rm = TRUE),
#             feeding_count = sum(feeding, na.rm = TRUE),
#             on_ship_count = sum(on_ship, na.rm = TRUE),
#             in_hand_count = sum(in_hand, na.rm = TRUE),
#             fly_by_count = sum(fly_by, na.rm = TRUE)) 

library(leaflet)

getColor <- function(sight_map) {
  sapply(sight_map$bird_type, function(sight_map) {
  case_when(sight_map$bird_type == "Tropicbird" ~ "#50e2ea",
            sight_map$bird_type == "Tern" ~ "#4edae5",
            sight_map$bird_type == "Skua" ~ "#4bd2df",
            sight_map$bird_type == "Sheathbill" ~ "#49cada",
            sight_map$bird_type == "Shearwater" ~ "#47c2d4",
            sight_map$bird_type == "Shag" ~ "#45bbcf",
            sight_map$bird_type == "Seabird" ~ "#42b3c9",
            sight_map$bird_type == "Procellaria" ~ "#40abc4",
            sight_map$bird_type == "Prion" ~ "#3ea3be", 
            sight_map$bird_type == "Petrel" ~ "#3b9bb9", 
            sight_map$bird_type == "Penguin" ~ "#3993b3",
            sight_map$bird_type == "Noddy" ~ "#378bae", 
            sight_map$bird_type == "Mollymawk" ~ "#3483a8", 
            sight_map$bird_type == "Jaeger" ~ "#327ba3", 
            sight_map$bird_type == "Gull" ~ "#30739d", 
            sight_map$bird_type == "Gannet" ~ "#2e6c98", 
            sight_map$bird_type == "Fulmar" ~ "#2b6492",
            sight_map$bird_type == "Frigatebird" ~ "#295c8d",
            sight_map$bird_type == "Cormorant" ~ "#275487",
            sight_map$bird_type == "Booby" ~ "#244c82", 
            sight_map$bird_type == "Albatross" ~ "#22447c",
            TRUE ~ sight_map$bird_type
    
  ) })
}


icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = list("Tropicbird" = "#50e2ea", "Tern" = "#4edae5", "Skua" = "#4bd2df", 
           "Sheathbill" = "#49cada", "Shearwater" = "#47c2d4", 
           "Shag" = "#45bbcf", "Seabird" = "#42b3c9", "Procellaria" = "#40abc4",
           "Prion" = "#3ea3be", "Petrel" = "#3b9bb9", "Penguin" = "#3993b3", 
           "Noddy" = "#378bae", "Mollymawk" = "#3483a8", "Jaeger" = "#327ba3", 
           "Gull" = "#30739d", "Gannet" = "#2e6c98", "Fulmar" = "#2b6492", 
           "Frigatebird" = "#295c8d", "Cormorant" = "#275487", 
           "Booby" = "#244c82", "Albatross" = "#22447c")
)


# test inputs so they look like what shiny will give us for user inputs.
input <- list(
  sight_input = "Cormorant"
)

sight_map <-   birds_21 %>% 
    filter(bird_type %in% input$sight_input)
  
sight_map%>% 
    leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addAwesomeMarkers(~long, ~lat, icon=icons, 
                    label = sight_map$common_name,
                    clusterOptions = markerClusterOptions()) 

  
 # Print the map

getColor <- function(quakes) { sapply(position$date, function(date) { case_when(str_detect(date, regex(“^196”, ignore_case = TRUE)) ~ “green”, str_detect(date, regex(“^197”, ignore_case = TRUE)) ~ “orange”, str_detect(date, regex(“^198”, ignore_case = TRUE)) ~ “blue”, str_detect(date, regex(“^199”, ignore_case = TRUE)) ~ “red”

) }) }

icons <- awesomeIcons( icon = ‘ios-close’, iconColor = ‘black’, library = ‘ion’, markerColor = getColor(position) )

leaflet(position) %>% addTiles() %>% addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

-45.91667 165.4000

Tab 4 Vessel Location tab

seabirds_cleaned_data

ship_data <- read_excel(here("raw_data/seabirds.xls"), 
                             sheet = "Ship data by record ID") %>% 
              clean_names()

position <- ship_data %>% 
  select(date, lat, long) %>%
  filter(!is.na(lat),
         !is.na(long)) %>% 
  group_by(date) %>% 
  summarise_if(is.numeric, mean)

position

ship_data %>% 
  select(date, lat, long) %>%
  filter(is.na(date))

tail(position)

leaflet(data = position) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(label = position$date, clusterOptions = markerClusterOptions()) 
  
 # Print the map

https://kateto.net/network-visualization

https://stackoverflow.com/questions/38432788/how-do-i-visualise-multiple-routes-using-leaflet-in-r

# https://rstudio.github.io/leaflet/markers.html
# first 20 quakes
df.20 <- quakes[1:20,]

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  if(date <= 1979-12-31) {
    "green"
  } else if(date <= 1989-12-31) {
    "orange"
  } else {
    "red"
  } })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  if(date %in% "^196") {
    "green"
  } else if(date  %in% "^197") {
    "orange"
  } else if(date  %in% "^198") {
    "blue"
  } else {
    "red"
  } })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  case_when(str_detect(date, 
             regex("^196", 
                   ignore_case = TRUE)) ~ "green",
            str_detect(date, 
             regex("^197", 
                   ignore_case = TRUE)) ~ "orange",
             str_detect(date, 
             regex("^198", 
                   ignore_case = TRUE)) ~ "blue",
             str_detect(date, 
             regex("^199", 
                   ignore_case = TRUE)) ~ "red"
    
  ) })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

# https://stackoverflow.com/questions/56362519/how-to-filter-date-range-for-routes-in-r-leaflet-shiny-app

library(dplyr)
library(shiny)
library(leaflet)
library(readxl)
library(RColorBrewer)
library(maps)

Warning: package ‘maps’ was built under R version 4.1.3

Attaching package: ‘maps’

The following object is masked from ‘package:purrr’:

    map

library(leaflet.extras)

Warning: package ‘leaflet.extras’ was built under R version 4.1.3

library(htmlwidgets)

Warning: package ‘htmlwidgets’ was built under R version 4.1.2

data_dots = read_csv("test4.csv")

Rows: 8 Columns: 14
-- Column specification ---------------------------------------------------
Delimiter: ","
chr (6): Name, ship_date, delivery_date, ShipmentID, Dcity, Origin
dbl (8): Dzip, Dlong, Dlat, Route, Seq, Ozip, Olong, Olat

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

ui <- bootstrapPage(
  tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
  leafletOutput("map", width = "100%", height = "100%"),
  absolutePanel(top = 10, right = 10,

                dateRangeInput("dateRange", "Date Range Input", start =  min(data_dots$ship_date), end = max(data_dots$ship_date))


  )
)

Warning: Couldn't coerce the `end` argument to a date string with format yyyy-mm-dd

server <- function(input, output) {

  #n <- 60
  qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual', ]
  col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))


  myMap = leaflet("map") %>% 
    addTiles(group = "Base") %>%
    addProviderTiles(providers$CartoDB.Positron, group = "Grey") %>%
    addResetMapButton()


  rv <- reactiveValues(
    filteredData =data_dots,
    ids = unique(data_dots$Route)
  )

  observeEvent(input$dateRange, 
               {rv$filteredData = data_dots[as.Date(data_dots$ship_date) >= input$dateRange[1] & as.Date(data_dots$ship_date) <= input$dateRange[2],]

               rv$ids = unique(rv$filteredData$Route)
               }

  )



  # Initiate the map
  output$map <- renderLeaflet({

    for (i in rv$ids) {
      #print(i)
      myMap = myMap %>%
        addPolylines(
          data = subset(rv$filteredData, Route == i),
          weight = 3,
          color = sample(col_vector, 1),
          opacity = 0.8,
          smoothFactor = 1,
          lng = ~Dlong, 
          lat = ~Dlat,
          highlight = highlightOptions(
            weight = 5,
            color = "blue",
            bringToFront = TRUE
          ),
          label = ~ as.character(ShipmentID),
          popup = ~ as.character(ShipmentID),
          group = "test"
        )

    }
    myMap


  })


}
shinyApp(ui = ui, server = server)

Listening on http://127.0.0.1:7348
Warning: Error in charToDate: character string is not in a standard unambiguous format
  [No stack trace available]

NA

data_dots %>% 
  mutate(ship_date = as.Date(ship_date, "%y/%m/%d"),
         delivery_date = as.Date(delivery_date, "%y/%m/%d"))

Error in mutate(., ship_date = as.Date(ship_date, "%y/%m/%d"), delivery_date = as.Date(delivery_date,  : 
  object 'data_dots' not found